library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ──────────────────────────────────────────────────────────────── tidyverse 1.3.2 ──✔ ggplot2 3.3.6 ✔ purrr 0.3.4
✔ tibble 3.1.8 ✔ dplyr 1.0.9
✔ tidyr 1.2.0 ✔ stringr 1.4.0
✔ readr 2.1.2 ✔ forcats 0.5.1── Conflicts ─────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
library(gganimate)
library(ggtern)
Registered S3 methods overwritten by 'ggtern':
method from
grid.draw.ggplot ggplot2
plot.ggplot ggplot2
print.ggplot ggplot2
--
Remember to cite, run citation(package = 'ggtern') for further info.
--
Attaching package: ‘ggtern’
The following objects are masked from ‘package:ggplot2’:
aes, annotate, ggplot, ggplot_build, ggplot_gtable, ggplotGrob, ggsave, layer_data,
theme_bw, theme_classic, theme_dark, theme_gray, theme_light, theme_linedraw,
theme_minimal, theme_void
obp_dynatts_df <- read_csv("resources/3_obp_dynatts_df.csv")
Rows: 106657 Columns: 24── Column specification ─────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (2): SECCODE, ATT
dbl (20): NO, OBPLOTNO, VAL, SHAREBAL, BPROFIT, SPROFIT, OBPMINTPRICE, OBPMAXTPRICE, CBOVOLtdcs, CSO...
dttm (1): DATETIMEMLLS
date (1): DATE
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
obp_dynatts_df
OBPlots_df <- read_csv("resources/2_OBPlots_df.csv")
Rows: 106657 Columns: 12── Column specification ─────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (2): SECCODE, BUYSELL
dbl (9): NO, TIME, ORDERNO, ACTION, PRICE, VOLUME, TRADENO, TRADEPRICE, OBPLOTNO
date (1): DATE
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
OBPlots_df
obp_dynatts_no_df <- left_join(obp_dynatts_df,
OBPlots_df %>% select(NO, TIME),
by = "NO") %>%
mutate(TIMEm = as.character(as.integer(TIME / 100000)),
TIME = as.character(TIME / 1000)) %>%
select(NO, SECCODE, DATETIMEMLLS, DATE, TIME, TIMEm, OBPLOTNO,
ATT, VAL, SHAREBAL, BPROFIT, SPROFIT, OBPMINTPRICE, OBPMAXTPRICE,
CBOVOLtdcs, CSOVOLtdcs, BOVOLtdcs, SOVOLtdcs, BTVOLtdcs, STVOLtdcs,
CBOVOLobpcs, CSOVOLobpcs, BOVOLobpcs, SOVOLobpcs, BTVOLobpcs, STVOLobpcs)
obp_dynatts_no_df
obp_dynatts_no_price_df <- left_join(obp_dynatts_no_df,
OBPlots_df %>% select(NO, PRICE, TRADEPRICE, VOLUME),
by="NO")
obp_dynatts_no_price_df
temp_df <- obp_dynatts_no_df %>%
spread(key = ATT, value = VAL) %>%
select(TIME, TIMEm, NO, OBPLOTNO, SOVOL, SOVOLtdcs, SOVOLobpcs)
# filter(OBPLOTNO == 129)
temp_df
ggplot(data = temp_df) +
# geom_point(mapping = aes(x = NO, y = SOVOL), color = "red") +
# geom_point(mapping = aes(x = NO, y = SOVOLtdcs), color = "green") +
geom_point(mapping = aes(x = NO, y = SOVOLobpcs), color = "blue")

# sm - val median by second
temp_sm_df <- temp_df %>%
group_by(TIMEm) %>%
summarise(SOVOLobpcs = median(SOVOLobpcs))
temp_sm_df
ggplot(data = temp_sm_df) +
geom_point(mapping = aes(x = TIMEm, y = SOVOLobpcs), color = "blue")

obp_atts_by_obp_df <- read_csv("resources/4_obp_atts_by_obp_df.csv")
Rows: 2028 Columns: 8── Column specification ─────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (2): SECCODE, BUYSELLOBP
dbl (4): OBPLOTNO, BUYSELLYIELD, OBPTDVOLRATIO, MINMAXRATIO
lgl (1): TRADESNOTRADES
date (1): DATE
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
obp_atts_by_obp_df
obp_atts_by_obp_df %>% arrange(desc(MINMAXRATIO))
# obp_atts_by_obp_df %>% arrange(BUYSELLYIELD)
pbegin <- obp_dynatts_no_price_df %>%
.$DATETIMEMLLS %>%
min(.)
print(pbegin)
[1] "2007-10-08 10:30:00 UTC"
pend <- obp_dynatts_no_price_df %>%
.$DATETIMEMLLS %>%
max(.)
print(pend)
[1] "2007-10-08 17:45:00 UTC"
pcolor <- function(att, plotno) {
color = ""
if (plotno == plotno) {
if (att == 'BOVOL') color = "green"
else if (att == 'SOVOL') color = "red"
else if (att == 'BTVOL' | att == 'STVOL') color = "blue"
else color = "white"
} else {
if (att == 'BOVOL') color = "green"#"aquamarine"
else if (att == 'SOVOL') color = "red"#"coral"
else if (att == 'BTVOL' | att == 'STVOL') color = "blue" #"cadetblue1"
else color = "white"
}
# print(paste(att, plotno, color))
return(color)
}
td_df <- obp_dynatts_no_price_df %>%
filter((DATETIMEMLLS >= pbegin & DATETIMEMLLS <= pend) & (ATT == "BOVOL" | ATT == "SOVOL" | ATT == "BTVOL" | ATT == "STVOL") & PRICE > 2145.0 & PRICE < 2195.0) %>%
mutate(pcolor = map2(ATT, OBPLOTNO, ~pcolor(.x, .y)) %>% unlist())
td_df
# curplotno = 302
curplotno = 1869
# curplotno = 52
pbegin <- obp_dynatts_no_price_df %>%
filter(OBPLOTNO == curplotno) %>%
.$DATETIMEMLLS %>%
min(.)
pend <- obp_dynatts_no_price_df %>%
filter(OBPLOTNO == curplotno) %>%
.$DATETIMEMLLS %>%
max(.)
pcolor <- function(att, plotno) {
color = ""
if (plotno == curplotno) {
if (att == 'BOVOL') color = "green"
else if (att == 'SOVOL') color = "red"
else if (att == 'BTVOL' | att == 'STVOL') color = "#8031A7" #"blue"
else color = "white"
} else {
if (att == 'BOVOL') color = "aquamarine"
else if (att == 'SOVOL') color = "coral"
else if (att == 'BTVOL' | att == 'STVOL') color = "#004481" #"cadetblue1"
else color = "white"
}
# print(paste(att, plotno, color))
return(color)
}
pshape <- function(att, plotno) {
color = ""
if (plotno == curplotno) {
shape = 16
} else {
shape = 4
}
# print(paste(att, plotno, color))
return(shape)
}
psize <- function(att, plotno) {
color = ""
if (plotno == curplotno) {
size = 1.0
} else {
size = 0.5
}
# print(paste(att, plotno, color))
return(size)
}
plot_df_without_rep_by_vol <- obp_dynatts_no_price_df %>%
filter((DATETIMEMLLS >= pbegin & DATETIMEMLLS <= pend) & (ATT == "BOVOL" | ATT == "SOVOL" | ATT == "BTVOL" | ATT == "STVOL") & PRICE > 2145.0 & PRICE < 2205.0) %>%
mutate(pcolor = map2(ATT, OBPLOTNO, ~pcolor(.x, .y)) %>% unlist(),
pshape = map2(ATT, OBPLOTNO, ~pshape(.x, .y)) %>% unlist(),
psize = map2(ATT, OBPLOTNO, ~psize(.x, .y)) %>% unlist())
plot_df_without_rep_by_vol %>% filter(OBPLOTNO == curplotno)
# plot_df <- with(plot_df_without_rep_by_vol,
# plot_df_without_rep_by_vol[rep(1:nrow(plot_df_without_rep_by_vol),
# as.integer(log(VOLUME, base = 1.01)) + 1),])
plot_df <- plot_df_without_rep_by_vol
# dt1 <- td_df %>% filter(ATT != "BTVOL" & ATT != "STVOL")
# dt2 <- td_df %>% filter(ATT == "BTVOL" | ATT == "STVOL")
# ggplot() +
# geom_point(data = dt1, mapping = aes(x = TIME, y = PRICE), color = dt1$pcolor, shape = 4, size = 0.5) +
# geom_point(data = dt2, mapping = aes(x = TIME, y = PRICE), color = dt2$pcolor, shape = 4, size = 0.5)
dt11 <- plot_df %>% filter(OBPLOTNO != curplotno & ATT != "BTVOL" & ATT != "STVOL")
dt12 <- plot_df %>% filter(OBPLOTNO != curplotno & (ATT == "BTVOL" | ATT == "STVOL"))
dt21 <- plot_df %>% filter(OBPLOTNO == curplotno & ATT != "BTVOL" & ATT != "STVOL")
dt22 <- plot_df %>% filter(OBPLOTNO == curplotno & (ATT == "BTVOL" | ATT == "STVOL"))
ggplot() +
geom_point(data = dt11, mapping = aes(x = TIME, y = PRICE),
color = dt11$pcolor, shape = dt11$pshape, size = dt11$psize) +
geom_point(data = dt12, mapping = aes(x = TIME, y = PRICE),
color = dt12$pcolor, shape = dt12$pshape, size = dt12$psize) +
geom_point(data = dt21, mapping = aes(x = TIME, y = PRICE),
color = dt21$pcolor, shape = dt21$pshape, size = dt21$psize) +
geom_point(data = dt22, mapping = aes(x = TIME, y = PRICE),
color = dt22$pcolor, shape = dt22$pshape, size = dt22$psize)

dt_s <- plot_df %>% filter(OBPLOTNO != curplotno & ATT == "SOVOL")
dt_b <- plot_df %>% filter(OBPLOTNO != curplotno & ATT == "BOVOL")
dt_t <- plot_df %>% filter(OBPLOTNO != curplotno & (ATT == "BTVOL" | ATT == "STVOL"))
dt_cp_sb <- plot_df %>% filter(OBPLOTNO == curplotno & ATT != "BTVOL" & ATT != "STVOL")
dt_cp_t <- plot_df %>% filter(OBPLOTNO == curplotno & (ATT == "BTVOL" | ATT == "STVOL"))
# dt_s <- plot_df %>% filter(ATT == "SOVOL")
# dt_b <- plot_df %>% filter(ATT == "BOVOL")
# dt_t <- plot_df %>% filter(ATT == "BTVOL" | ATT == "STVOL")
# ggplot(dt_s, aes(x = NO, y = PRICE)) +
# stat_density2d(mapping = aes(fill = ..level..), alpha = .5,
# geom = "polygon", data = dt_s) +
# scale_fill_viridis_c() +
# # theme(legend.position = 'none') +
# scale_fill_distiller(palette = 'Reds')
# ggplot(dt_b, aes(x = NO, y = PRICE)) +
# stat_density2d(mapping = aes(fill = ..level..), alpha = .5,
# geom = "polygon", data = dt_b) +
# scale_fill_viridis_c() +
# # theme(legend.position = 'none') +
# scale_fill_distiller(palette = 'Greens')
ggplot(bind_rows(tibble(dt_s, gr = "s"), tibble(dt_b, gr = "b")), aes(x = NO, y = PRICE)) +
stat_density2d(geom = "polygon", aes(fill = gr, alpha = ..level..)) +
scale_fill_manual(values=c("s"="#FF0000", "b"="#00FF00")) +
geom_point(data = dt_t, mapping = aes(x = NO, y = PRICE),
color = dt_t$pcolor, shape = dt_t$pshape, size = dt_t$psize) +
geom_point(data = dt_cp_sb, mapping = aes(x = NO, y = PRICE),
color = dt_cp_sb$pcolor, shape = dt_cp_sb$pshape, size = dt_cp_sb$psize) +
geom_point(data = dt_cp_t, mapping = aes(x = NO, y = PRICE),
color = dt_cp_t$pcolor, shape = dt_cp_t$pshape, size = dt_cp_t$psize)

ggplot(bind_rows(tibble(dt_b, gr = "s"), tibble(dt_s, gr = "b")), aes(x = NO, y = PRICE)) +
stat_density2d(geom = "polygon", aes(fill = gr, alpha = ..level..)) +
scale_fill_manual(values=c("b"="#FF0000", "s"="#00FF00")) +
geom_point(data = dt_t, mapping = aes(x = NO, y = PRICE),
color = dt_t$pcolor, shape = dt_t$pshape, size = dt_t$psize) +
geom_point(data = dt_cp_sb, mapping = aes(x = NO, y = PRICE),
color = dt_cp_sb$pcolor, shape = dt_cp_sb$pshape, size = dt_cp_sb$psize) +
geom_point(data = dt_cp_t, mapping = aes(x = NO, y = PRICE),
color = dt_cp_t$pcolor, shape = dt_cp_t$pshape, size = dt_cp_t$psize)

ggplot(bind_rows(tibble(dt_s, gr = "s"), tibble(dt_b, gr = "b")), aes(x = NO, y = PRICE)) +
stat_density2d(geom = "density2d", aes(color = gr)) +
scale_color_manual(values=c("s"="#FF0000", "b"="#00FF00")) +
geom_point(data = dt_t, mapping = aes(x = NO, y = PRICE),
color = dt_t$pcolor, shape = dt_t$pshape, size = dt_t$psize) +
geom_point(data = dt_cp_sb, mapping = aes(x = NO, y = PRICE),
color = dt_cp_sb$pcolor, shape = dt_cp_sb$pshape, size = dt_cp_sb$psize) +
geom_point(data = dt_cp_t, mapping = aes(x = NO, y = PRICE),
color = dt_cp_t$pcolor, shape = dt_cp_t$pshape, size = dt_cp_t$psize) +
theme_bw()

td_first_30minute_df <- td_df %>%
filter(TIME < 1100000)
td_first_30minute_df
static_plot <- ggplot() +
geom_point(data = td_first_30minute_df,
mapping = aes(x = TIME, y = PRICE),
color = td_first_30minute_df$pcolor,
shape = 4,
size = 0.5)
static_plot

# animation <- static_plot +
# transition_states(NO, transition_length = 1, state_length = 1) +
# ease_aes('linear') +
# shadow_mark() +
# view_follow()
# animation
# obp30mins_gif <- animate(animation,
# fps = 10,
# duration = 10,
# width = 800, height = 400,
# renderer = gifski_renderer("./2gif/obp30mins.gif"))
---
title: "OBPlots"
output: html_notebook
---

```{r}
library(tidyverse)
library(gganimate)
library(ggtern)
```


```{r}
obp_dynatts_df <- read_csv("resources/3_obp_dynatts_df.csv")
obp_dynatts_df
```

```{r}
OBPlots_df <- read_csv("resources/2_OBPlots_df.csv")
OBPlots_df
```

```{r}
obp_dynatts_no_df <- left_join(obp_dynatts_df, 
                            OBPlots_df %>% select(NO, TIME), 
                            by = "NO") %>% 
  mutate(TIMEm = as.character(as.integer(TIME / 100000)),
         TIME = as.character(TIME / 1000)) %>% 
  select(NO, SECCODE, DATETIMEMLLS, DATE, TIME, TIMEm, OBPLOTNO, 
         ATT, VAL, SHAREBAL, BPROFIT, SPROFIT, OBPMINTPRICE, OBPMAXTPRICE,
         CBOVOLtdcs, CSOVOLtdcs, BOVOLtdcs, SOVOLtdcs, BTVOLtdcs, STVOLtdcs,
         CBOVOLobpcs, CSOVOLobpcs, BOVOLobpcs, SOVOLobpcs, BTVOLobpcs, STVOLobpcs)
obp_dynatts_no_df
```

```{r}
obp_dynatts_no_price_df <- left_join(obp_dynatts_no_df, 
                                     OBPlots_df %>% select(NO, PRICE, TRADEPRICE, VOLUME), 
                                     by="NO")
obp_dynatts_no_price_df
```

```{r}
temp_df <- obp_dynatts_no_df %>% 
  spread(key = ATT, value = VAL) %>% 
  select(TIME, TIMEm, NO, OBPLOTNO, SOVOL, SOVOLtdcs, SOVOLobpcs)
  # filter(OBPLOTNO == 129)
temp_df
```

```{r}
ggplot(data = temp_df) + 
  # geom_point(mapping = aes(x = NO, y = SOVOL), color = "red") +
  # geom_point(mapping = aes(x = NO, y = SOVOLtdcs), color = "green") +
  geom_point(mapping = aes(x = NO, y = SOVOLobpcs), color = "blue")
```

```{r}
# sm - val median by second

temp_sm_df <- temp_df %>% 
  group_by(TIMEm) %>% 
  summarise(SOVOLobpcs = median(SOVOLobpcs))
temp_sm_df
```


```{r}
ggplot(data = temp_sm_df) + 
  geom_point(mapping = aes(x = TIMEm, y = SOVOLobpcs), color = "blue")
```

```{r}
obp_atts_by_obp_df <- read_csv("resources/4_obp_atts_by_obp_df.csv")
obp_atts_by_obp_df
```

```{r}
obp_atts_by_obp_df %>% arrange(desc(MINMAXRATIO))
# obp_atts_by_obp_df %>% arrange(BUYSELLYIELD)
```


```{r}
pbegin <- obp_dynatts_no_price_df %>% 
  .$DATETIMEMLLS %>% 
  min(.)
print(pbegin)

pend <- obp_dynatts_no_price_df %>% 
  .$DATETIMEMLLS %>% 
  max(.) 
print(pend)

pcolor <- function(att, plotno) {
  color = ""
  if (plotno == plotno) {
    if (att == 'BOVOL') color = "green"
    else if (att == 'SOVOL') color = "red"
    else if (att == 'BTVOL' | att == 'STVOL') color = "blue"
    else color = "white"
  } else {
    if (att == 'BOVOL') color = "green"#"aquamarine"
    else if (att == 'SOVOL') color = "red"#"coral"
    else if (att == 'BTVOL' | att == 'STVOL') color = "blue" #"cadetblue1"
    else color = "white"
  }
  # print(paste(att, plotno, color))
  return(color)
}
td_df <- obp_dynatts_no_price_df %>% 
  filter((DATETIMEMLLS >= pbegin & DATETIMEMLLS <= pend) & (ATT == "BOVOL" | ATT == "SOVOL" | ATT == "BTVOL" | ATT == "STVOL") & PRICE > 2145.0 & PRICE < 2195.0) %>%
  mutate(pcolor = map2(ATT, OBPLOTNO, ~pcolor(.x, .y)) %>% unlist())
td_df
```

```{r}
# curplotno = 302
curplotno = 1869
# curplotno = 52

pbegin <- obp_dynatts_no_price_df %>%
  filter(OBPLOTNO == curplotno) %>%
  .$DATETIMEMLLS %>%
  min(.)

pend <- obp_dynatts_no_price_df %>%
  filter(OBPLOTNO == curplotno) %>%
  .$DATETIMEMLLS %>%
  max(.)

pcolor <- function(att, plotno) {
  color = ""
  if (plotno == curplotno) {
    if (att == 'BOVOL') color = "green"
    else if (att == 'SOVOL') color = "red"
    else if (att == 'BTVOL' | att == 'STVOL') color = "#8031A7" #"blue"
    else color = "white"
  } else {
    if (att == 'BOVOL') color = "aquamarine"
    else if (att == 'SOVOL') color = "coral"
    else if (att == 'BTVOL' | att == 'STVOL') color = "#004481" #"cadetblue1"
    else color = "white"
  }
  # print(paste(att, plotno, color))
  return(color)
}

pshape <- function(att, plotno) {
  color = ""
  if (plotno == curplotno) {
    shape = 16
  } else {
    shape = 4
  }
  # print(paste(att, plotno, color))
  return(shape)
}

psize <- function(att, plotno) {
  color = ""
  if (plotno == curplotno) {
    size = 1.0
  } else {
    size = 0.5
  }
  # print(paste(att, plotno, color))
  return(size)
}

plot_df_without_rep_by_vol <- obp_dynatts_no_price_df %>% 
  filter((DATETIMEMLLS >= pbegin & DATETIMEMLLS <= pend) & (ATT == "BOVOL" | ATT == "SOVOL" | ATT == "BTVOL" | ATT == "STVOL") & PRICE > 2145.0 & PRICE < 2205.0) %>%
  mutate(pcolor = map2(ATT, OBPLOTNO, ~pcolor(.x, .y)) %>% unlist(),
         pshape = map2(ATT, OBPLOTNO, ~pshape(.x, .y)) %>% unlist(),
         psize = map2(ATT, OBPLOTNO, ~psize(.x, .y)) %>% unlist())
plot_df_without_rep_by_vol %>% filter(OBPLOTNO == curplotno)
```

```{r}
# plot_df <-  with(plot_df_without_rep_by_vol,
#                  plot_df_without_rep_by_vol[rep(1:nrow(plot_df_without_rep_by_vol),
#                                                 as.integer(log(VOLUME, base = 1.01)) + 1),])
plot_df <-  plot_df_without_rep_by_vol
```


```{r}
# dt1 <- td_df %>% filter(ATT != "BTVOL" & ATT != "STVOL")
# dt2 <- td_df %>% filter(ATT == "BTVOL" | ATT == "STVOL")
# ggplot() + 
#   geom_point(data = dt1, mapping = aes(x = TIME, y = PRICE), color = dt1$pcolor, shape = 4, size = 0.5) +
#   geom_point(data = dt2, mapping = aes(x = TIME, y = PRICE), color = dt2$pcolor, shape = 4, size = 0.5)
```

```{r}
dt11 <- plot_df %>% filter(OBPLOTNO != curplotno & ATT != "BTVOL" & ATT != "STVOL")
dt12 <- plot_df %>% filter(OBPLOTNO != curplotno & (ATT == "BTVOL" | ATT == "STVOL"))
dt21 <- plot_df %>% filter(OBPLOTNO == curplotno & ATT != "BTVOL" & ATT != "STVOL")
dt22 <- plot_df %>% filter(OBPLOTNO == curplotno & (ATT == "BTVOL" | ATT == "STVOL"))
ggplot() + 
  geom_point(data = dt11, mapping = aes(x = TIME, y = PRICE), 
             color = dt11$pcolor, shape = dt11$pshape, size = dt11$psize) + 
  geom_point(data = dt12, mapping = aes(x = TIME, y = PRICE), 
             color = dt12$pcolor, shape = dt12$pshape, size = dt12$psize) + 
  geom_point(data = dt21, mapping = aes(x = TIME, y = PRICE), 
             color = dt21$pcolor, shape = dt21$pshape, size = dt21$psize) + 
  geom_point(data = dt22, mapping = aes(x = TIME, y = PRICE), 
             color = dt22$pcolor, shape = dt22$pshape, size = dt22$psize)
```

```{r}
dt_s <- plot_df %>% filter(OBPLOTNO != curplotno & ATT == "SOVOL")
dt_b <- plot_df %>% filter(OBPLOTNO != curplotno & ATT == "BOVOL")
dt_t <- plot_df %>% filter(OBPLOTNO != curplotno & (ATT == "BTVOL" | ATT == "STVOL"))
dt_cp_sb <- plot_df %>% filter(OBPLOTNO == curplotno & ATT != "BTVOL" & ATT != "STVOL")
dt_cp_t <- plot_df %>% filter(OBPLOTNO == curplotno & (ATT == "BTVOL" | ATT == "STVOL"))

# dt_s <- plot_df %>% filter(ATT == "SOVOL")
# dt_b <- plot_df %>% filter(ATT == "BOVOL")
# dt_t <- plot_df %>% filter(ATT == "BTVOL" | ATT == "STVOL")

```

```{r}
# ggplot(dt_s, aes(x = NO, y = PRICE)) +
#   stat_density2d(mapping = aes(fill = ..level..), alpha = .5,
#                  geom = "polygon", data = dt_s) + 
#   scale_fill_viridis_c() + 
#   # theme(legend.position = 'none') +
#   scale_fill_distiller(palette = 'Reds')
```

```{r}
# ggplot(dt_b, aes(x = NO, y = PRICE)) +
#   stat_density2d(mapping = aes(fill = ..level..), alpha = .5,
#                  geom = "polygon", data = dt_b) + 
#   scale_fill_viridis_c() + 
#   # theme(legend.position = 'none') +
#   scale_fill_distiller(palette = 'Greens')
```


```{r}
ggplot(bind_rows(tibble(dt_s, gr = "s"), tibble(dt_b, gr = "b")), aes(x = NO, y = PRICE)) +
  stat_density2d(geom = "polygon", aes(fill = gr, alpha = ..level..)) + 
  scale_fill_manual(values=c("s"="#FF0000", "b"="#00FF00")) +
  geom_point(data = dt_t, mapping = aes(x = NO, y = PRICE), 
             color = dt_t$pcolor, shape = dt_t$pshape, size = dt_t$psize) +
  geom_point(data = dt_cp_sb, mapping = aes(x = NO, y = PRICE), 
             color = dt_cp_sb$pcolor, shape = dt_cp_sb$pshape, size = dt_cp_sb$psize) +
  geom_point(data = dt_cp_t, mapping = aes(x = NO, y = PRICE), 
             color = dt_cp_t$pcolor, shape = dt_cp_t$pshape, size = dt_cp_t$psize)
```

```{r}
ggplot(bind_rows(tibble(dt_b, gr = "s"), tibble(dt_s, gr = "b")), aes(x = NO, y = PRICE)) +
  stat_density2d(geom = "polygon", aes(fill = gr, alpha = ..level..)) + 
  scale_fill_manual(values=c("b"="#FF0000", "s"="#00FF00")) +
  geom_point(data = dt_t, mapping = aes(x = NO, y = PRICE), 
             color = dt_t$pcolor, shape = dt_t$pshape, size = dt_t$psize) +
  geom_point(data = dt_cp_sb, mapping = aes(x = NO, y = PRICE), 
             color = dt_cp_sb$pcolor, shape = dt_cp_sb$pshape, size = dt_cp_sb$psize) +
  geom_point(data = dt_cp_t, mapping = aes(x = NO, y = PRICE), 
             color = dt_cp_t$pcolor, shape = dt_cp_t$pshape, size = dt_cp_t$psize)
```

```{r}
ggplot(bind_rows(tibble(dt_s, gr = "s"), tibble(dt_b, gr = "b")), aes(x = NO, y = PRICE)) +
  stat_density2d(geom = "density2d", aes(color = gr)) + 
  scale_color_manual(values=c("s"="#FF0000", "b"="#00FF00")) +
  geom_point(data = dt_t, mapping = aes(x = NO, y = PRICE), 
             color = dt_t$pcolor, shape = dt_t$pshape, size = dt_t$psize) +
  geom_point(data = dt_cp_sb, mapping = aes(x = NO, y = PRICE), 
             color = dt_cp_sb$pcolor, shape = dt_cp_sb$pshape, size = dt_cp_sb$psize) +
  geom_point(data = dt_cp_t, mapping = aes(x = NO, y = PRICE), 
             color = dt_cp_t$pcolor, shape = dt_cp_t$pshape, size = dt_cp_t$psize) +
  theme_bw()
```

```{r}
td_first_30minute_df <- td_df %>% 
  filter(TIME < 1100000)
td_first_30minute_df
```

```{r}
static_plot <- ggplot() + 
  geom_point(data = td_first_30minute_df, 
             mapping = aes(x = TIME, y = PRICE), 
             color = td_first_30minute_df$pcolor, 
             shape = 4, 
             size = 0.5)
static_plot
```

```{r}

```


```{r}
# animation <- static_plot +
#   transition_states(NO, transition_length = 1, state_length = 1) +
#   ease_aes('linear') +
#   shadow_mark() +
#   view_follow()
# animation
```

```{r}
# obp30mins_gif <- animate(animation, 
#                  fps = 10, 
#                  duration = 10,
#                  width = 800, height = 400, 
#                  renderer = gifski_renderer("./2gif/obp30mins.gif"))
```

